Abstract:
Effective drug combination prediction is crucial for the achievement of drug discovery, but it is a
challenging task due to drug drug interactions and potential adverse drug reactions. This study
presents an innovative technique named DDI-KGAT, which employs attention mechanisms to
identify crucial characteristics and interrelationships between drugs and various entities, including
targets and genes, using a knowledge graph-based strategy. By leveraging associated relations in
the knowledge graph, our model adeptly captures drugs and their potential surroundings, thus
extracting semantic relations and higher order structures of graph. The KEGG dataset is utilized
in evaluating the model's effectiveness, and is compared to other state of the art techniques. The
outcomes demonstrate that KGAT outperforms these methods. Additionally, our approach has
several advantages, including simplicity, interpretability, and low-dimensional complexity,
making it a favorable tool for accelerating the drug discovery and development. By identifying
novel drug combinations with improved efficacy and safety profiles, our approach has the
capability to improve the patient outcomes and support safer drug development. Our study
highlights the potential of attention mechanisms in knowledge graph-based drug combination
prediction, and we believe that KGAT framework has the potential to be a valuable foundation for
future research in this field.
